Magyar Bancorp (MGYR) Provision for Credit Losses (2009 - 2026)
Magyar Bancorp (MGYR) posted Provision for Credit Losses of $351,000 for fiscal Q3 2026 (quarter ended Jun 30, 2026), up 192.5% from $120,000 a year earlier and up 37.1% from the prior quarter.
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Analysis
Magyar Bancorp (MGYR) Provision for Credit Losses (2009 - 2026) Analysis & Trends
For the trailing twelve months through Jun 30, 2026, Provision for Credit Losses at Magyar Bancorp was $932,000, up 565.7% year-over-year; for FY2025 (ended Sep 30, 2025), it was $653,000, up 258.8% from FY2024.
- Annual Provision for Credit Losses shows a five-year compound annual growth rate of -17.1% (FY2020 to FY2025).
- In prior fiscal years, Magyar Bancorp's Provision for Credit Losses was $182,000 in FY2024 (-52.2%), $381,000 in FY2023 (+25.3%), $304,000 in FY2022 (-81.3%) and $1.63 million in FY2021 (-2.2%).
- The fiscal Q3 2026 figure stands as the highest quarterly Provision for Credit Losses since fiscal Q1 2024.
- On a year-over-year basis, Provision for Credit Losses increased in two of the last four quarters, with growth averaging 83.9%.
- The strongest year-over-year quarter for Provision for Credit Losses in the past five years was fiscal Q2 2026, with growth of 265.7%; the weakest was fiscal Q2 2022, with a decline of 84.8%.
- According to Business Quant data, Provision for Credit Losses for the three prior fiscal quarters was $256,000 (Q2 2026), $71,000 (Q1 2026) and $254,000 (Q4 2025).
Peer Set
Peer Comparison
| # | Company | Market Cap | Enterprise Value | Gross Profit (Qtr) | Provisions (Qtr) |
|---|---|---|---|---|---|
| 1 | Jpmorgan Chase | 920.14 Bn | 897.90 Bn | - | 2.74 Bn |
| 2 | Banco Santander Chile | 407.42 Bn | -7,109.68 Bn | - | 838.45 Bn |
| 3 | Bank Of America | 353.20 Bn | -2,022.91 Bn | - | 1.24 Bn |
| 4 | Hsbc Holdings | 307.88 Bn | -2,967.28 Bn | - | - |
| 5 | Morgan Stanley | 298.41 Bn | -244.41 Bn | - | 103.00 Mn |
| 6 | Royal Bank Of Canada | 283.46 Bn | -546.54 Bn | - | 717.20 Mn |
| 7 | Goldman Sachs | 264.45 Bn | -3,478.99 Bn | - | 368.00 Mn |
| 8 | Mitsubishi Ufj Financial | 259.19 Bn | -159.36 Bn | 5.24 Bn | - |
| 9 | Wells Fargo & Company | 258.28 Bn | 93.72 Bn | - | 1.03 Bn |
| 10 | Magyar Bancorp | 142.06 Mn | 142.06 Mn | - | - |
Historic Data
Download Data
Historic Data
| Date | Value |
|---|---|
| Jun 30, 2026 | 351,000.00 |
| Mar 31, 2026 | 256,000.00 |
| Dec 31, 2025 | 71,000.00 |
| Sep 30, 2025 | 254,000.00 |
| Jun 30, 2025 | 120,000.00 |
| Mar 31, 2025 | 70,000.00 |
| Dec 31, 2024 | 209,000.00 |
| Sep 30, 2024 | -259,000.00 |
| Jun 30, 2024 | -54,000.00 |
| Mar 31, 2024 | -74,000.00 |
| Dec 31, 2023 | 481,000.00 |
| Sep 30, 2023 | -51,000.00 |
| Jun 30, 2023 | -81,000.00 |
| Mar 31, 2023 | 195,000.00 |
| Dec 31, 2022 | 317,000.00 |
| Sep 30, 2022 | -72,000.00 |
| Jun 30, 2022 | 205,000.00 |
| Mar 31, 2022 | 71,000.00 |
| Dec 31, 2021 | 101,000.00 |
| Sep 30, 2021 | 276,000.00 |
API Access
Magyar Bancorp Provision for Credit Losses API
Pull this series into your own models, spreadsheets and apps with the Business Quant
Historical Metrics API. The request below matches the chart above — change the
frequency, period or values and it follows. Swap YOUR_API_KEY for your own key.
https://data.businessquant.com/historic?slug=provision-for-credit-losses&ticker=MGYR&period=max&api_key=YOUR_API_KEYimport requests
url = "https://data.businessquant.com/historic"
params = {"slug": "provision-for-credit-losses", "ticker": "MGYR", "period": "max", "api_key": "YOUR_API_KEY"}
data = requests.get(url, params=params).json()const res = await fetch("https://data.businessquant.com/historic?slug=provision-for-credit-losses&ticker=MGYR&period=max&api_key=YOUR_API_KEY");
const data = await res.json();